This invention relates generally to signal processing systems for monitoring assets, and, in particular, to an impulse monitoring system: apparatus and method for monitoring impulse events of assets such as rotating and reciprocating machines.
Machine operation causes characteristic sounds as a result of mechanical movement, forces acting on bearing surfaces, and process fluid and gas flows. These sounds are a result of energy being expended in the machine. Hence, the term impulse events is used since loose parts, excessive clearances, sticking parts, inadvertent metal-to-metal contacts, and other events cause many of the destructive events that can occur in a machine. These events result in the knocks, bangs, clanks, that indicate a machine problem. Hence, these sounds are characteristic of an impulse similar to the machine being struck by a hammer.
Historically, the determination whether these sounds were normal or not was done by the human ear listening and comparing the present sound with a previously remembered sound. The human ear is quite good at detecting a wide frequency range of sounds, detecting relative magnitudes, and discerning patterns or irregularities in patterns of sounds. However, among other thing, the ear has difficulty detecting sounds, particularly low level sounds, amidst adjoining loud noise.
Technology has allowed sensing machine operation sounds by using inertial sensors sensing acceleration or velocity of, for example, a machine case of a machine or by using microphones to detect sound pressure in the vicinity of the machine to remove the inconsistency of the human ear. Systems employing these sensors can detect and record sounds undetectable by the human ear particularly, lower levels sounds amidst adjoining loud noise.
However, to continuously sample a sound to detect the complete range of frequencies emitted, to compare them over a complete cycle of a machine, and to maintain a history for comparison is problematic in that it requires an enormous amount of data storage. Even by taking a periodic sample of the sound having enough data to make a comparison with historical data requires large amounts of storage.
One specific example of a machine that exhibits noisy normal operation is a reciprocating compressor which typically includes an installation of accelerometers above the crosshead and velocity sensors on the crankcase to detect large impulse events that indicate that the compressor is experiencing a problem. To detect these events, the detection level had to be set above the normal operating level of the compressor. With the detection set above normal operation, the identification of emerging problems is done by observing the dynamic waveform data to try to identify lower level impulse events and to correlate them to compressor operation to determine the probable cause.
However, this approach is problematic since the acceleration waveform has significant high frequency content such that if the waveform of a complete cycle was observed at a sample rate that was low enough to cause aliasing of the waveform the high frequency portion and the accompanying impulse event could be lost. On the other hand, if the sample rate were increased to see the high frequency components, the entire cycle could not be seen and only the very early part of the cycle could be seen. In both of these scenarios, the impulse events could be missed when looking at the waveform.
Hence, there is a need for an apparatus and method that solves the problem of determining impulse events of a machine particularly amidst adjoining loud noise, that solves the problem of the requirement for large amounts of data storage, and that solves the problem of signal processing resulting in the loss of high frequency portions and accompanying impulse events of machines.